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Research

Our research revolves around machine learning models and methods for big data over networks. The data arising in many important big data applications, ranging from social networks to network medicine, consist of high-dimensional data points related by an intrinsic (complex) network structure. In order to jointly leverage the information conveyed in the network structure as well as the statistical power contained in high-dimensional data points, we study networked exponential families. For the accurate learning of such networked exponential families, we borrow statistical strength, via the intrinsic network structure, across the dataset. A powerful algorithmic toolbox for designing learning algorithms is provided by convex optimization methods. Modern convex optimization methods are appealing for big data applications as they can be implemented as highly scalable message passing protocols.